SoroushVahidi/scidocs huggingface dataset:
Single-Image Noise Level Estimation for Blind Denoising
(2013)

Noise level is an important parameter to many image processing applications. For example, the performance of an image denoising algorithm can be much degraded due to the poor noise level estimation. Most existing denoising algorithms simply assume the noise level is known that largely prevents them from practical use. Moreover, even with the given true noise level, these denoising algorithms still cannot achieve the best performance, especially for scenes with rich texture. In this paper, we propose a patch-based noise level estimation algorithm and suggest that the noise level parameter should be tuned according to the scene complexity. Our approach includes the process of selecting low-rank patches without high frequency components from a single noisy image. The selection is based on the gradients of the patches and their statistics. Then, the noise level is estimated from the selected patches using principal component analysis. Because the true noise level does not always provide the best performance for nonblind denoising algorithms, we further tune the noise level parameter for nonblind denoising. Experiments demonstrate that both the accuracy and stability are superior to the state of the art noise level estimation algorithm for various scenes and noise levels.
Causal Structure Learning and Inference : A Selective Review
(2013)

In this paper we give a review of recent causal inference methods. First, we discuss methods for causal structure learning from observational data when confounders are not present and have a close look at methods for exact identifiability. We then turn to methods which allow for a mix of observational and interventional data, where we also touch on active learning strategies. We also discuss methods which allow arbitrarily complex structures of hidden variables. Second, we present approaches for estimating the interventional distribution and causal effects given the (true or estimated) causal structure. We close with a note on available software and two examples on real data.
Activity recognition using a single accelerometer placed at the wrist or ankle.
(2013)

PURPOSE
Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities.


METHODS
Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated.


RESULTS
With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%.


CONCLUSIONS
A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.
Toward Rapid Development of Multi-Party Virtual Human Negotiation Scenarios
(2011)

This paper reports on an ongoing effort to enable the rapid development of multi-party virtual human negotiation scenarios. We present a case study in which a new scenario supporting negotiation between two human role players and two virtual humans was developed over a period of 12 weeks. We discuss the methodology and development process that were employed, from storyline design through role play and iterative development of the virtual humans’ semantic and task representations and natural language processing capabilities. We analyze the effort, expertise, and time required for each development step, and discuss opportunities to further streamline the development process.
Deep Convolutional Neural Networks [Lecture Notes]
(2018)

Neural networks are a subset of the field of artificial intelligence (AI). The predominant types of neural networks used for multidimensional signal processing are deep convolutional neural networks (CNNs). The term deep refers generically to networks having from a "few" to several dozen or more convolution layers, and deep learning refers to methodologies for training these systems to automatically learn their functional parameters using data representative of a specific problem domain of interest. CNNs are currently being used in a broad spectrum of application areas, all of which share the common objective of being able to automatically learn features from (typically massive) data bases and to generalize their responses to circumstances not encountered during the learning phase. Ultimately, the learned features can be used for tasks such as classifying the types of signals the CNN is expected to process. The purpose of this "Lecture Notes" article is twofold: 1) to introduce the fundamental architecture of CNNs and 2) to illustrate, via a computational example, how CNNs are trained and used in practice to solve a specific class of problems.
Nearest prototype classifier designs: An experimental study
(2001)

We compare eleven methods for finding prototypes upon which to base the nearest Ž prototype classifier. Four methods for prototype selection are discussed: Wilson Hart a . condensation error-editing method , and three types of combinatorial search random search, genetic algorithm, and tabu search. Seven methods for prototype extraction are discussed: unsupervised vector quantization, supervised learning vector quantization Ž . with and without training counters , decision surface mapping, a fuzzy version of vector quantization, c-means clustering, and bootstrap editing. These eleven methods can be usefully divided two other ways: by whether they employ preor postsupervision; and by whether the number of prototypes found is user-defined or ‘‘automatic.’’ Generalization error rates of the 11 methods are estimated on two synthetic and two real data sets. Offering the usual disclaimer that these are just a limited set of experiments, we feel confident in asserting that presupervised, extraction methods offer a better chance for success to the casual user than postsupervised, selection schemes. Finally, our calculations do not suggest that methods which find the ‘‘best’’ number of prototypes ‘‘automatically’’ are superior to methods for which the user simply specifies the number of prototypes. 2001 John Wiley & Sons, Inc.
Synthesizing evidence in software engineering research
(2010)

Synthesizing the evidence from a set of studies that spans many countries and years, and that incorporates a wide variety of research methods and theoretical perspectives, is probably the single most challenging task of performing a systematic review. In this paper, we perform a tertiary review to assess the types and methods of research synthesis in systematic reviews in software engineering. Almost half of the 31 studies included in our review did not contain any synthesis; of the ones that did, two thirds performed a narrative or a thematic synthesis. The results show that, despite the focus on systematic reviews, there is, currently, limited attention to research synthesis in software engineering. This needs to change and a repertoire of synthesis methods needs to be an integral part of systematic reviews to increase their significance and utility for research and practice.
Spectral–Spatial Classification and Shape Features for Urban Road Centerline Extraction
(2014)

This letter presents a two-step method for urban main road extraction from high-resolution remotely sensed imagery by integrating spectral-spatial classification and shape features. In the first step, spectral-spatial classification segments the imagery into two classes, i.e., the road class and the nonroad class, using path openings and closings. The local homogeneity of the gray values obtained by local Geary's C is then fused with the road class. In the second step, the road class is refined by using shape features. The experimental results indicated that the proposed method was able to achieve a comparatively good performance in urban main road extraction.
Automatic Optimization Computational Method for Unconventional S . W . A . T . H . Ships Resistance
(2011)

The paper illustrates the main theoretical and computational aspects of an automatic computer based procedure for the parametric shape optimization of a particular unconventional hull typology: that for a catamaran S.W.A.T.H. ship. The goal of the integrated computational procedure is to find the best shape of the submerged hulls of a new U.S.V. (Unmanned Surface Vehicle) S.W.A.T.H. (Small Waterplane Area Twin Hull) vessel, in terms of minimum wave pattern resistance. After dealing with the theoretical aspects the papers presents the numerical aspects of the main software module of the automatic procedure, which integrates a parametric generation routine for innovative and unconventional S.W.A.T.H. (Small Waterplane Area Twin Hull) vessel geometry, a multi-objective, globally convergent and constrained, optimization algorithm and a Computational Fluid Dynamic (C.F.D.) solver. The integrated process is able to find the best shape of the submerged hull of the vessel, subject to the total displaced volume constraint. The hydrodynamic computation is carried out by means of a free surface potential flow method and it is addressed to find the value of wave resistance of each hull variant. Results of the application of the described computational procedure are presented for two optimization cases and the obtained best shapes are compared with a conventional one, featuring a typical torpedo-shaped body, proving the effectiveness of the method in reducing the resistance by a considerable extent, in the order of 40 percent. Keywords—S.W.A.T.H., B.E.M., Wave Resistance, Parametric Modeling, Optimization, Genetic Algorithms
BRAD 1.0: Book reviews in Arabic dataset
(2016)

The availability of rich datasets is a pre-requisite for proposing robust sentiment analysis systems. A variety of such datasets exists in English language. However, it is rare or nonexistent for the Arabic language except for a recent LABR dataset, which consists of a little bit over 63,000 book reviews extracted from. Goodreads. com. We introduce BRAD 1.0, the largest Book Reviews in Arabic Dataset for sentiment analysis and machine language applications. BRAD comprises of almost 510,600 book records. Each record corresponds for a single review and has the review in Arabic language and the reviewer's rating on a scale of 1 to 5 stars. In this paper, we present and describe the properties of BRAD. Further, we provide two versions of BRAD: the complete unbalanced dataset and the balanced version of BRAD. Finally, we implement four sentiment analysis classifiers based on this dataset and report our findings. When training and testing the classifiers on BRAD as opposed to LABR, an improvement rate growth of 46% is reported. The highest accuracy attained is 91%. Our core contribution is to make this benchmark-dataset available and accessible to the research community on Arabic language.
An integrated approach to testing complex systems
(2003)

The increasing complexity of today’s testing scenarios for complex systems demands an integrated, open, and flexible approach to support the management of the overall test process. “Classical” model-based testing approaches, where a complete and precise formal specification serves as a reference for automatic test generation, are often impractical. Reasons are, on the one hand, the absence of a suitable formal specification. As complex systems are composed of several components, either hardware or software, often pre-built and third party, it is unrealistic to assume that a formal specification exists a priori. On the other hand, a sophisticated test execution environment is needed that can handle distributed test cases. This is because the test actions and observations can take place on different subsystems of the overall system. This thesis presents a novel approach to the integrated testing of complex systems. Our approach offers a coarse grained test environment, realized in terms of a component-based test design on top of a library of elementary but intuitively understandable test case fragments. The relations between the fragments are treated orthogonally, delivering a test design and execution environment enhanced by means of light-weight formal verification methods. In this way we are able to shift the test design issues from total experts of the system and the used test tools to experts of the system’s logic only. We illustrate the practical usability of our approach by means of industrial case studies in two different application domains: Computer Telephony Integrated solutions and Web-based applications. As an enhancement of our integrated test approach we provide an algorithm for generating approximate models for complex systems a posteriori. This is done by optimizing a standard machine learning algorithm according to domain-specific structural properties, i.e. properties like prefix-closeness, input-determinism, as well as independency and symmetries of events. The resulting models can never be exact, i.e. reflect the complete and correct behaviour of the considered system. Nevertheless they can be useful in practice, to represent the cumulative knowledge of the system in a consistent description.
Axial-Ratio-Bandwidth Enhancement of a Microstrip-Line-Fed Circularly Polarized Annular-Ring Slot Antenna
(2011)

The design of a new microstrip-line-fed wideband circularly polarized (CP) annular-ring slot antenna (ARSA) is proposed. Compared with existing ring slot antennas, the ARSAs designed here possess much larger CP bandwidths. The main features of the proposed design include a wider ring slot, a pair of grounded hat-shaped patches, and a deformed bent feeding microstrip line. The ARSAs designed using FR4 substrates in the L and S bands have 3-dB axial-ratio bandwidths (ARBWs) of as large as 46% and 56%, respectively, whereas the one using an RT5880 substrate in the L band, 65%. In these 3-dB axial-ratio bands, impedance matching with VSWR ≤ 2 is also achieved.
A Practical Approach to Differential Private Learning
(2018)

Applying differential private learning to real-world data is currently unpractical. Differential privacy (DP) introduces extra hyper-parameters for which no thorough good practices exist, while manually tuning these hyper-parameters on private data results in low privacy guarantees. Furthermore, the exact guarantees provided by differential privacy for machine learning models are not well understood. Current approaches use undesirable post-hoc privacy attacks on models to assess privacy guarantees. To improve this situation, we introduce three tools to make DP machine learning more practical. First, two sanity checks for differential private learning are proposed. These sanity checks can be carried out in a centralized manner before training, do not involve training on the actual data and are easy to implement. Additionally, methods are proposed to reduce the effective number of tuneable privacy parameters by making use of an adaptive clipping bound. Lastly, existing methods regarding large batch training and differential private learning are combined. It is demonstrated that this combination improves model performance within a constant privacy budget.
Privacy as a fuzzy concept: A new conceptualization of privacy for practitioners
(2015)

• Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain.
TG13 flowchart for the management of acute cholangitis and cholecystitis.
(2013)

We propose a management strategy for acute cholangitis and cholecystitis according to the severity assessment. For Grade I (mild) acute cholangitis, initial medical treatment including the use of antimicrobial agents may be sufficient for most cases. For non-responders to initial medical treatment, biliary drainage should be considered. For Grade II (moderate) acute cholangitis, early biliary drainage should be performed along with the administration of antibiotics. For Grade III (severe) acute cholangitis, appropriate organ support is required. After hemodynamic stabilization has been achieved, urgent endoscopic or percutaneous transhepatic biliary drainage should be performed. In patients with Grade II (moderate) and Grade III (severe) acute cholangitis, treatment for the underlying etiology including endoscopic, percutaneous, or surgical treatment should be performed after the patient's general condition has been improved. In patients with Grade I (mild) acute cholangitis, treatment for etiology such as endoscopic sphincterotomy for choledocholithiasis might be performed simultaneously, if possible, with biliary drainage. Early laparoscopic cholecystectomy is the first-line treatment in patients with Grade I (mild) acute cholecystitis while in patients with Grade II (moderate) acute cholecystitis, delayed/elective laparoscopic cholecystectomy after initial medical treatment with antimicrobial agent is the first-line treatment. In non-responders to initial medical treatment, gallbladder drainage should be considered. In patients with Grade III (severe) acute cholecystitis, appropriate organ support in addition to initial medical treatment is necessary. Urgent or early gallbladder drainage is recommended. Elective cholecystectomy can be performed after the improvement of the acute inflammatory process. Free full-text articles and a mobile application of TG13 are available via http://www.jshbps.jp/en/guideline/tg13.html.
Disease Trajectory Maps
(2016)

Medical researchers are coming to appreciate that many diseases are in fact complex, heterogeneous syndromes composed of subpopulations that express different variants of a related complication. Longitudinal data extracted from individual electronic health records (EHR) offer an exciting new way to study subtle differences in the way these diseases progress over time. In this paper, we focus on answering two questions that can be asked using these databases of longitudinal EHR data. First, we want to understand whether there are individuals with similar disease trajectories and whether there are a small number of degrees of freedom that account for differences in trajectories across the population. Second, we want to understand how important clinical outcomes are associated with disease trajectories. To answer these questions, we propose the Disease Trajectory Map (DTM), a novel probabilistic model that learns low-dimensional representations of sparse and irregularly sampled longitudinal data. We propose a stochastic variational inference algorithm for learning the DTM that allows the model to scale to large modern medical datasets. To demonstrate the DTM, we analyze data collected on patients with the complex autoimmune disease, scleroderma. We find that DTM learns meaningful representations of disease trajectories and that the representations are significantly associated with important clinical outcomes.
Wideband H-Plane Horn Antenna Based on Ridge Substrate Integrated Waveguide (RSIW)
(2012)

A substrate integrated waveguide (SIW) H-plane sectoral horn antenna, with significantly improved bandwidth, is presented. A tapered ridge, consisting of a simple arrangement of vias on the side flared wall within the multilayer substrate, is introduced to enlarge the operational bandwidth. A simple feed configuration is suggested to provide the propagating wave for the antenna structure. The proposed antenna is simulated by two well-known full-wave packages, Ansoft HFSS and CST Microwave Studio, based on segregate numerical methods. Close agreement between simulation results is reached. The designed antenna shows good radiation characteristics and low VSWR, lower than 2.5, for the whole frequency range of 18-40 GHz.
Multiscale Approaches To Music Audio Feature Learning
(2013)

Content-based music information retrieval tasks are typically solved with a two-stage approach: features are extracted from music audio signals, and are then used as input to a regressor or classifier. These features can be engineered or learned from data. Although the former approach was dominant in the past, feature learning has started to receive more attention from the MIR community in recent years. Recent results in feature learning indicate that simple algorithms such as K-means can be very effective, sometimes surpassing more complicated approaches based on restricted Boltzmann machines, autoencoders or sparse coding. Furthermore, there has been increased interest in multiscale representations of music audio recently. Such representations are more versatile because music audio exhibits structure on multiple timescales, which are relevant for different MIR tasks to varying degrees. We develop and compare three approaches to multiscale audio feature learning using the spherical K-means algorithm. We evaluate them in an automatic tagging task and a similarity metric learning task on the Magnatagatune dataset.
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
(2018)

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy.